tensorflow - 如何在 Keras 中使用模型子类化？

Question

在顺序 API 中编写以下模型：

config = {
    'learning_rate': 0.001,
    'lstm_neurons':32,
    'lstm_activation':'tanh',
    'dropout_rate': 0.08,
    'batch_size': 128,
    'dense_layers':[
      {'neurons': 32, 'activation': 'relu'},
      {'neurons': 32, 'activation': 'relu'},
    ]
}

def get_model(num_features, output_size):
    opt = Adam(learning_rate=0.001)
    model = Sequential()
    model.add(Input(shape=[None,num_features], dtype=tf.float32, ragged=True))
    model.add(LSTM(config['lstm_neurons'], activation=config['lstm_activation']))
    model.add(BatchNormalization()) 
    if 'dropout_rate' in config:
      model.add(Dropout(config['dropout_rate']))

    for layer in config['dense_layers']:
      model.add(Dense(layer['neurons'], activation=layer['activation']))
      model.add(BatchNormalization()) 
      if 'dropout_rate' in layer:
        model.add(Dropout(layer['dropout_rate']))

    model.add(Dense(output_size, activation='sigmoid'))
    model.compile(loss='mse', optimizer=opt, metrics=['mse'])
    print(model.summary())
    return model

使用分布式训练框架时，我需要将语法转换为使用模型子类化。我查看了文档，但不知道该怎么做。

Answer 1

这是一个等效的子类实现。虽然我没有测试。

import tensorflow as tf 

# your config 
config = {
    'learning_rate': 0.001,
    'lstm_neurons':32,
    'lstm_activation':'tanh',
    'dropout_rate': 0.08,
    'batch_size': 128,
    'dense_layers':[
      {'neurons': 32, 'activation': 'relu'},
      {'neurons': 32, 'activation': 'relu'},
    ]
}

# Subclassed API Model 
class MySubClassed(tf.keras.Model):
    def __init__(self, output_size):
        super(MySubClassed, self).__init__()
        self.lstm = tf.keras.layers.LSTM(config['lstm_neurons'],
                                     activation=config['lstm_activation'])
        self.bn = tf.keras.layers.BatchNormalization()
        
        if 'dropout_rate' in config:
            self.dp1 = tf.keras.layers.Dropout(config['dropout_rate'])
            self.dp2 = tf.keras.layers.Dropout(config['dropout_rate'])
            self.dp3 = tf.keras.layers.Dropout(config['dropout_rate'])

        for layer in config['dense_layers']:
            self.dense1 = tf.keras.layers.Dense(layer['neurons'],
                                        activation=layer['activation'])
            self.bn1 = tf.keras.layers.BatchNormalization()
            self.dense2 = tf.keras.layers.Dense(layer['neurons'],
                                        activation=layer['activation'])
            self.bn2 = tf.keras.layers.BatchNormalization()
            
        self.out = tf.keras.layers.Dense(output_size, 
                                        activation='sigmoid')
            
    
    def call(self, inputs, training=True, **kwargs):
        x = self.lstm(inputs)
        x = self.bn(x)
        
        if 'dropout_rate' in config:
            x = self.dp1(x)
        
        x = self.dense1(x)
        x = self.bn1(x)
        if 'dropout_rate' in config:
            x = self.dp2(x)

        x = self.dense2(x)
        x = self.bn2(x)
        if 'dropout_rate' in config:
            x = self.dp3(x)

        return self.out(x)

    # A convenient way to get model summary 
    # and plot in subclassed api
    def build_graph(self, raw_shape):
        x = tf.keras.layers.Input(shape=(None, raw_shape), 
                                         ragged=True)
        return tf.keras.Model(inputs=[x], 
                              outputs=self.call(x))

构建和编译 mdoel

 s = MySubClassed(output_size=1)
 s.compile(
     loss = 'mse',
     metrics = ['mse'],
     optimizer = tf.keras.optimizers.Adam(learning_rate=0.001))

通过一些张量来创建权重（检查）。

raw_input = (16, 16, 16)
y = s(tf.ones(shape=(raw_input))) 

print("weights:", len(s.weights))
print("trainable weights:", len(s.trainable_weights))

weights: 21
trainable weights: 15

总结和情节

总结和可视化模型图。

s.build_graph(16).summary()

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, None, 16)]        0         
_________________________________________________________________
lstm (LSTM)                  (None, 32)                6272      
_________________________________________________________________
batch_normalization (BatchNo (None, 32)                128       
_________________________________________________________________
dropout (Dropout)            (None, 32)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 32)                1056      
_________________________________________________________________
batch_normalization_3 (Batch (None, 32)                128       
_________________________________________________________________
dropout_1 (Dropout)          (None, 32)                0         
_________________________________________________________________
dense_3 (Dense)              (None, 32)                1056      
_________________________________________________________________
batch_normalization_4 (Batch (None, 32)                128       
_________________________________________________________________
dropout_2 (Dropout)          (None, 32)                0         
_________________________________________________________________
dense_4 (Dense)              (None, 1)                 33        
=================================================================
Total params: 8,801
Trainable params: 8,609
Non-trainable params: 192

tf.keras.utils.plot_model(
    s.build_graph(16),
    show_shapes=True,
    show_dtype=True,
    show_layer_names=True,
    rankdir="TB",
)